Wessel P. Bruinsma

39 papers A* 11A 3Journal 24Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A foundation model for the Earth system. Nat. Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Anna Allen, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan A. Weyn, Haiyu Dong, Jayesh K. Gupta, Kit Thambiratnam, Alexander T. Archibald, Chun-Chieh Wu, Elizabeth Heider, Max Welling, Richard E. Turner, Paris Perdikaris
2025	J	`jnl`	Accurate and scalable exchange-correlation with deep learning. CoRR Giulia Luise, Chin-Wei Huang, Thijs Vogels, Derk P. Kooi, Sebastian Ehlert, Stephanie Lanius, Klaas J. H. Giesbertz, Amir Karton, Deniz Gunceler, Megan Stanley, Wessel P. Bruinsma, Lin Huang, Xinran Wei, José Garrido Torres, Abylay Katbashev, Rodrigo Chavez Zavaleta, Bálint Máté, Sékou-Oumar Kaba, Roberto Sordillo, Yingrong Chen, David B. Williams-Young, Christopher M. Bishop, Jan Hermann, Rianne van den Berg, Paola Gori-Giorgi
2025	J	`jnl`	End-to-end data-driven weather prediction. Nat. Anna Allen, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, Michael Herzog, Nicholas D. Lane, Matthew Chantry, J. Scott Hosking, Richard E. Turner
2024	J	`jnl`	Aardvark Weather: end-to-end data-driven weather forecasting. CoRR Anna Vaughan, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, Michael Herzog, Nicholas D. Lane, J. Scott Hosking, Richard E. Turner
2024	A*	`conf`	Approximately Equivariant Neural Processes. NeurIPS Matthew Ashman, Cristiana Diaconu, Adrian Weller, Wessel P. Bruinsma, Richard E. Turner
2024	J	`jnl`	Approximately Equivariant Neural Processes. CoRR Matthew Ashman, Cristiana Diaconu, Adrian Weller, Wessel P. Bruinsma, Richard E. Turner
2024	J	`jnl`	Aurora: A Foundation Model of the Atmosphere. CoRR Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan A. Weyn, Haiyu Dong, Anna Vaughan, Jayesh K. Gupta, Kit Thambiratnam, Alexander T. Archibald, Elizabeth Heider, Max Welling, Richard E. Turner, Paris Perdikaris
2024	J	`jnl`	Convolutional Conditional Neural Processes. CoRR Wessel P. Bruinsma
2024	A*	`conf`	Noise-Aware Differentially Private Regression via Meta-Learning. NeurIPS Ossi Räisä, Stratis Markou, Matthew Ashman, Wessel P. Bruinsma, Marlon Tobaben, Antti Honkela, Richard E. Turner
2024	J	`jnl`	Noise-Aware Differentially Private Regression via Meta-Learning. CoRR Ossi Räisä, Stratis Markou, Matthew Ashman, Wessel P. Bruinsma, Marlon Tobaben, Antti Honkela, Richard E. Turner
2024	A*	`conf`	Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants. ICML Isabel Chien, Wessel P. Bruinsma, Javier González Hernández, Richard E. Turner
2024	A*	`conf`	Translation Equivariant Transformer Neural Processes. ICML Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner
2024	J	`jnl`	Translation Equivariant Transformer Neural Processes. CoRR Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner
2023	A*	`conf`	Autoregressive Conditional Neural Processes. ICLR Wessel P. Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner
2023	J	`jnl`	Autoregressive Conditional Neural Processes. CoRR Wessel P. Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner
2022	J	`jnl`	A Note on the Chernoff Bound for Random Variables in the Unit Interval. CoRR Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt
2022	J	`jnl`	Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement. CoRR Tom R. Andersson, Wessel P. Bruinsma, Stratis Markou, James Requeima, Alejandro Coca-Castro, Anna Vaughan, Anna-Louise Ellis, Matthew Lazzara, Daniel C. Jones, J. Scott Hosking, Richard E. Turner
2022	J	`jnl`	Challenges and Pitfalls of Bayesian Unlearning. CoRR Ambrish Rawat, James Requeima, Wessel P. Bruinsma, Richard E. Turner
2022	A	`conf`	Modelling Non-Smooth Signals with Complex Spectral Structure. AISTATS Wessel P. Bruinsma, Martin Tegner, Richard E. Turner
2022	J	`jnl`	Modelling Non-Smooth Signals with Complex Spectral Structure. CoRR Wessel P. Bruinsma, Martin Tegner, Richard E. Turner
2022	A*	`conf`	Practical Conditional Neural Process Via Tractable Dependent Predictions. ICLR Stratis Markou, James Requeima, Wessel P. Bruinsma, Anna Vaughan, Richard E. Turner
2022	J	`jnl`	Practical Conditional Neural Processes Via Tractable Dependent Predictions. CoRR Stratis Markou, James Requeima, Wessel P. Bruinsma, Anna Vaughan, Richard E. Turner
2022	A*	`conf`	Sparse Gaussian Process Hyperparameters: Optimize or Integrate? NeurIPS Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl Edward Rasmussen
2022	J	`jnl`	Sparse Gaussian Process Hyperparameters: Optimize or Integrate? CoRR Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl E. Rasmussen
2022	A	`conf`	Wide Mean-Field Bayesian Neural Networks Ignore the Data. AISTATS Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez
2022	J	`jnl`	Wide Mean-Field Bayesian Neural Networks Ignore the Data. CoRR Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez
2021	J	`jnl`	Efficient Gaussian Neural Processes for Regression. CoRR Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner
2021	A*	`conf`	How Tight Can PAC-Bayes be in the Small Data Regime? NeurIPS Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner
2021	J	`jnl`	How Tight Can PAC-Bayes be in the Small Data Regime? CoRR Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner
2021	J	`jnl`	The Gaussian Neural Process. CoRR Wessel P. Bruinsma, James Requeima, Andrew Y. K. Foong, Jonathan Gordon, Richard E. Turner
2020	A*	`conf`	Convolutional Conditional Neural Processes. ICLR Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner
2020	A*	`conf`	Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. NeurIPS Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner
2020	J	`jnl`	Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. CoRR Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner
2020	A*	`conf`	Scalable Exact Inference in Multi-Output Gaussian Processes. ICML Wessel P. Bruinsma, Eric Perim, William Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner
2019	J	`jnl`	Convolutional Conditional Neural Processes. CoRR Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner
2019	—	`conf`	GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models. AABI Pavel Berkovich, Eric Perim, Wessel P. Bruinsma
2019	J	`jnl`	GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models. CoRR Pavel Berkovich, Eric Perim, Wessel P. Bruinsma
2019	J	`jnl`	Scalable Exact Inference in Multi-Output Gaussian Processes. CoRR Wessel P. Bruinsma, Eric Perim, William Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner
2019	A	`conf`	The Gaussian Process Autoregressive Regression Model (GPAR). AISTATS James Requeima, William Tebbutt, Wessel P. Bruinsma, Richard E. Turner